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Related Concept Videos

Active Filters01:25

Active Filters

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Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
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Bandpass Sampling01:17

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
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Upsampling01:22

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Frequency-Domain Interpretation of PD Control01:24

Frequency-Domain Interpretation of PD Control

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Proportional-Derivative (PD) controllers are widely used in fan control systems to improve stability and performance. A fan control system can be effectively represented using a Bode plot to illustrate the impact of a PD controller through its transfer function. The Bode plot visually conveys how PD control modifies the fan's response across various frequencies, providing a frequency domain interpretation of the controller's behavior.
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Passive Filters01:27

Passive Filters

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Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
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Using empirical wavelet transform to speed up selective filtered active noise control system.

Shulin Wen1, Woon-Seng Gan1, Dongyuan Shi1

  • 1Digital Signal Processing Lab, School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore.

The Journal of the Acoustical Society of America
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an improved active noise control method using empirical wavelet transform for faster and more effective noise reduction. The new approach enhances performance for both stationary and non-stationary noise, overcoming limitations of existing systems.

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Area of Science:

  • Acoustics and Signal Processing
  • Adaptive Systems

Background:

  • Traditional active noise control (ANC) faces challenges with gradual adaptation and divergence, limiting its application scope.
  • Selective ANC improves noise reduction by selecting pre-trained filters but requires rapid convergence for non-stationary noise.
  • Existing methods struggle with instant convergence and efficient feature extraction for dynamic noise environments.

Purpose of the Study:

  • To enhance the speed and performance of selective filtered active noise control (SFANC) systems.
  • To introduce a novel method for instantaneous frequency extraction of primary noise.
  • To reduce the computational and storage requirements of SFANC systems.

Main Methods:

  • Introduced empirical wavelet transform (EWT) for accurate and instantaneous frequency extraction of primary noise.
  • Utilized the boundary of the first intrinsic mode function (IMF) as an instant signal feature.
  • Developed a strategy to select the optimal pre-trained control filter based on the nearest boundary feature.
  • Implemented a selective filtered active noise control system incorporating EWT for real-time adaptation.

Main Results:

  • The proposed algorithm achieves instant noise control, overcoming output saturation instability.
  • Demonstrated superior noise reduction performance compared to conventional and standard selective ANC algorithms.
  • Significantly reduced the storage requirements for the pre-trained control filter library.
  • Achieved more concentrated energy distribution in noise attenuation.

Conclusions:

  • The integration of EWT with SFANC offers a robust solution for real-time noise control.
  • The proposed method provides faster tracking and improved noise reduction, especially for non-stationary noise.
  • This approach broadens the applicability of ANC systems by addressing limitations in adaptation and convergence speed.